Position Sizing CalculatorThis is an intuitive risk management tool with a minimalist design.
This calculator will determine your position size per trade, profit, loss, risk/reward ratio and leverage if any.
It will calculate your leverage if you are trading financial instruments e.g. Mini Futures , Turbo Warrants etc. that have a financing level.
Tip: Use this as a complement to the Long/Short Position tool.
Provide the following inputs to get a calculation:
- Position type
- Account balance
- Risk per trade percentage
- Financing level (if any for leveraged instruments), else let it be 0
- Entry price
- Target price
- Stopp loss price
You can also choose the color of the output text, its background and position in the chart window.
Enjoy!
在腳本中搜尋"如何用wind搜索股票的发行价和份数"
NormalizedOscillatorsLibrary "NormalizedOscillators"
Collection of some common Oscillators. All are zero-mean and normalized to fit in the -1..1 range. Some are modified, so that the internal smoothing function could be configurable (for example, to enable Hann Windowing, that John F. Ehlers uses frequently). Some are modified for other reasons (see comments in the code), but never without a reason. This collection is neither encyclopaedic, nor reference, however I try to find the most correct implementation. Suggestions are welcome.
rsi2(upper, lower) RSI - second step
Parameters:
upper : Upwards momentum
lower : Downwards momentum
Returns: Oscillator value
Modified by Ehlers from Wilder's implementation to have a zero mean (oscillator from -1 to +1)
Originally: 100.0 - (100.0 / (1.0 + upper / lower))
Ignoring the 100 scale factor, we get: upper / (upper + lower)
Multiplying by two and subtracting 1, we get: (2 * upper) / (upper + lower) - 1 = (upper - lower) / (upper + lower)
rms(src, len) Root mean square (RMS)
Parameters:
src : Source series
len : Lookback period
Based on by John F. Ehlers implementation
ift(src) Inverse Fisher Transform
Parameters:
src : Source series
Returns: Normalized series
Based on by John F. Ehlers implementation
The input values have been multiplied by 2 (was "2*src", now "4*src") to force expansion - not compression
The inputs may be further modified, if needed
stoch(src, len) Stochastic
Parameters:
src : Source series
len : Lookback period
Returns: Oscillator series
ssstoch(src, len) Super Smooth Stochastic (part of MESA Stochastic) by John F. Ehlers
Parameters:
src : Source series
len : Lookback period
Returns: Oscillator series
Introduced in the January 2014 issue of Stocks and Commodities
This is not an implementation of MESA Stochastic, as it is based on Highpass filter not present in the function (but you can construct it)
This implementation is scaled by 0.95, so that Super Smoother does not exceed 1/-1
I do not know, if this the right way to fix this issue, but it works for now
netKendall(src, len) Noise Elimination Technology by John F. Ehlers
Parameters:
src : Source series
len : Lookback period
Returns: Oscillator series
Introduced in the December 2020 issue of Stocks and Commodities
Uses simplified Kendall correlation algorithm
Implementation by @QuantTherapy:
rsi(src, len, smooth) RSI
Parameters:
src : Source series
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
vrsi(src, len, smooth) Volume-scaled RSI
Parameters:
src : Source series
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
This is my own version of RSI. It scales price movements by the proportion of RMS of volume
mrsi(src, len, smooth) Momentum RSI
Parameters:
src : Source series
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
Inspired by RocketRSI by John F. Ehlers (Stocks and Commodities, May 2018)
rrsi(src, len, smooth) Rocket RSI
Parameters:
src : Source series
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
Inspired by RocketRSI by John F. Ehlers (Stocks and Commodities, May 2018)
Does not include Fisher Transform of the original implementation, as the output must be normalized
Does not include momentum smoothing length configuration, so always assumes half the lookback length
mfi(src, len, smooth) Money Flow Index
Parameters:
src : Source series
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
lrsi(src, in_gamma, len) Laguerre RSI by John F. Ehlers
Parameters:
src : Source series
in_gamma : Damping factor (default is -1 to generate from len)
len : Lookback period (alternatively, if gamma is not set)
Returns: Oscillator series
The original implementation is with gamma. As it is impossible to collect gamma in my system, where the only user input is length,
an alternative calculation is included, where gamma is set by dividing len by 30. Maybe different calculation would be better?
fe(len) Choppiness Index or Fractal Energy
Parameters:
len : Lookback period
Returns: Oscillator series
The Choppiness Index (CHOP) was created by E. W. Dreiss
This indicator is sometimes called Fractal Energy
er(src, len) Efficiency ratio
Parameters:
src : Source series
len : Lookback period
Returns: Oscillator series
Based on Kaufman Adaptive Moving Average calculation
This is the correct Efficiency ratio calculation, and most other implementations are wrong:
the number of bar differences is 1 less than the length, otherwise we are adding the change outside of the measured range!
For reference, see Stocks and Commodities June 1995
dmi(len, smooth) Directional Movement Index
Parameters:
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
Based on the original Tradingview algorithm
Modified with inspiration from John F. Ehlers DMH (but not implementing the DMH algorithm!)
Only ADX is returned
Rescaled to fit -1 to +1
Unlike most oscillators, there is no src parameter as DMI works directly with high and low values
fdmi(len, smooth) Fast Directional Movement Index
Parameters:
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
Same as DMI, but without secondary smoothing. Can be smoothed later. Instead, +DM and -DM smoothing can be configured
doOsc(type, src, len, smooth) Execute a particular Oscillator from the list
Parameters:
type : Oscillator type to use
src : Source series
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
Chande Momentum Oscillator (CMO) is RSI without smoothing. No idea, why some authors use different calculations
LRSI with Fractal Energy is a combo oscillator that uses Fractal Energy to tune LRSI gamma, as seen here: www.prorealcode.com
doPostfilter(type, src, len) Execute a particular Oscillator Postfilter from the list
Parameters:
type : Oscillator type to use
src : Source series
len : Lookback period
Returns: Oscillator series
Vertical LinesThis script plots vertical lines on charts or indicators. Unfortunately pinescript is lacking a vertical line plotting function. Vertical lines are useful to mark events, such as crossover of levels, indicators signals or as a time marker.
After searching the internet for a long time and trying different scripts, this script is the simplest and visually the best. You would think that plotting a vertical line would be relatively easy, it is not! I thank the unknow author for sharing this solution and now I will share it on tradingview to make it readily available to anybody that needs it.
RSI crossover signals are used as an example in this script. When the RSI crosses over 70 or below 30, the script plots a red or green vertical line.
The script plots a vertical line as a histogram bar. The histogram bar must have a height.
Setting the height near infinity like 1e20 will cover all the ranges from top to bottom in most charts, but doesn't work all the time. If the chart range is small in values, the line is not plotted or the chart is visually compressed because the top of the bar is also a data point in the chart. Another solution is to find the highest point in the chart and multiply it by a number from 2 to 10 to set the top of the histogram bar. But this solution doesn't work if the line is drawn in the indicator window. additionally if the chart or indicator includes negative values, a histogram bar with a negative height must be concatenated to the histogram bar with a positive height to cover the positive and negative range.
It would seem intuitive to include a vertical plot function since it is very useful and pinescript already has a horizontal line plot function called Hline. But pinescript is becoming less intuitive, and redundant. A case in point is Version 4 variable declaration and naming, it less intuitive and more redundant than previous versions. I beg Tradingview to adopt a more refined scripting language such as Matlab or Python for charting purposes. These languages can be easily ported to other analysis programs for AI or statistical analysis.
K's Volatility BandsVolatility bands come in all shapes and forms contrary to what is believed. Bollinger bands remain the principal indicator in the volatility bands family. K's Volatility bands is an attempt at optimizing the original bands. Below is the method of calculation:
* We must first start by calculating a rolling measure based on the average between the highest high and the lowest low in the last specified lookback window. This will give us a type of moving average that tracks the market price. The specificity here is that when the market does not make higher highs nor lower lows, the line will be flat. A flat line can also be thought of as a magnet of the price as the ranging property could hint to a further sideways movement.
* The K’s volatility bands assume the worst with volatility and thus will take the maximum volatility for a given lookback period. Unlike the Bollinger bands which will take the latest volatility calculation every single step of time, K’s volatility bands will suppose that we must be protected by the maximum of volatility for that period which will give us from time to time stable support and resistance levels.
Therefore, the difference between the Bollinger bands and K's volatility bands are as follows:
* Bollinger Bands' formula calculates a simple moving average on the closing prices while K's volatility bands' formula calculates the average of the highest highs and the lowest lows.
* Bollinger Bands' formula calculates a simple standard deviation on the closing prices while K's volatility bands' formula calculates the highest standard deviation for the lookback period.
Applying the bands is similar to applying any other volatility bands. We can list the typical strategies below:
* The range play strategy : This is the usual reversal strategy where we buy whenever the price hits the lower band and sell short whenever it hits the upper band.
* The band re-entry strategy : This strategy awaits the confirmation that the price has recognized the band and has shaped a reaction around it and has reintegrated the whole envelope. It may be slightly lagging in nature but it may filter out bad trades.
* Following the trend strategy : This is a controversial strategy that is the opposite of the first one. It assumes that whenever the upper band is surpassed, a buy signal is generated and whenever the lower band is broken, a sell signal is generated.
* Combination with other indicators : The bands can be combined with other technical indicators such as the RSI in order to have more confirmation. This is however no guarantee that the signals will improve in quality.
* Specific strategy on K’s volatility bands : This one is similar to the first range play strategy but it adds the extra filter where the trade has a higher conviction if the median line is flat. The reason for this is that a flat line means that no higher highs nor lower lows have been made and therefore, we may be in a sideways market which is a fertile ground for mean-reversion strategies.
Daily Settlement (BM&FBOVESPA B3 FUTURES)This script is simple designed to plot the daily settlement to any Securities traded on B3, Brazilian stock exchange.
The daily settlement is an important price where position traders are adjusted every day. This adjustment is defined by the exchange itself every day at approximately 4 pm, with an average of all trades in this window.
We consider that the settlement is a region of "money spent", where every day, some player "woke up" in long or in short at that price. As this is a region of "money spent", traders should give significant attention when traded at this price.
Directional Movement w/Hann Slope Change SignalModified version of
Presented here is code for the "Directional Movement w/Hann" indicator originally conceived by John Ehlers. The code is also published in the December 2021 issue of Trader's Tips by Technical Analysis of Stocks & Commodities (TASC) magazine.
John Ehlers is continuing to revamp old indictors with Hann windowing. The original script uses zero line cross to signal buy/sell in this modified version buy/sell is signaled based on slope change, where signal is generated on with previous value is greater/less than current value
If current > previous = buy and if current < previous = sell
Relative StrengthRelative Strength show the quotient (ratio) between a numerator (Your Security) and a common denominator (SPX, BTC, ...).
I inspired myself from already established indicators. But I've made my own because I had some issues with others.
Sometimes the moving average weren't fixed but where floating through the window.
Sometimes the colors (Up, Down) were inaccurate because the compiler didn't notice infinitesimal changes (0.0001 > 0.0002)
Thanks.
(The Relative Strength indicator is at the top of the chart.)
Higher Time Frame Chart OverlayHello All,
This script gets OHLC values from any security and Higher/Same time frame you set, then creates the chart including last 10 candles. it shows Symbol name, Time Frame, Highest/Lowest level of last 10 candles and Close Price at the right side of the chart as well. Closing price text color changes by the real-time candle of the related symbol and time frame. The all this was made using the Tables in Pine and the chart location doesn't change even if you change the size of main chart window.
Almost everything can be change as you want. You can change/set:
- Colors of Body and Top/Bottom Wicks separately
- The Height of each Cell
- The Width of Body and Wicks
- The Background and Frame color
- Enable/disable Status Panel (if you disable Status Panel then only candle chart is shown)
- Location of Status Panel
- Text color and Text size
- The Background color of Status Panel
Some examples:
The info shown in Status Panel:
You can change The Height of each Cell and The Width of Body and Wicks
You can change colors:
You can change location of the chart:
If you add the script more than once then you can see the charts for different symbols and time frames: (This may slow down your chart)
If you right-click on the script and choose "Visual Order" => "Bring to front" then it will be better visually:
P.S. Using this script may slow down your chart, especially if you add it more than once
Enjoy!
MAD indicator Enchanced (MADH, inspired by J.Ehlers)This oscillator was inspired by the recent J. Ehler's article (Stocks & Commodities V. 39:11 (24–26): The MAD Indicator, Enhanced by John F. Ehlers). Basically, it shows the difference between two move averages, an "enhancement" made by the author in the last version comes down to replacement SMA to a weighted average that uses Hann windowing. I took the liberty to add colors, ROC line (well, you know, no shorts when ROC's negative and no long's when positive, etc), and optional usage of PVT (price-volume trend) as the source (instead of just price).
Volume X-ray [LucF]█ OVERVIEW
This tool analyzes the relative size of volume reported on intraday vs EOD (end of day) data feeds on historical bars. If you use volume data to make trading decisions, it can help you improve your understanding of its nature and quality, which is especially important if you trade on intraday timeframes.
I often mention, when discussing volume analysis, how it's important for traders to understand the volume data they are using: where it originates, what it includes and does not include. By helping you spot sizeable differences between volume reported on intraday and EOD data feeds for any given instrument, "Volume X-ray" can point you to instruments where you might want to research the causes of the difference.
█ CONCEPTS
The information used to build a chart's historical bars originates from data providers (exchanges, brokers, etc.) who often maintain distinct historical feeds for intraday and EOD timeframes. How volume data is assembled for intraday and EOD feeds varies with instruments, brokers and exchanges. Variations between the two feeds — or their absence — can be due to how instruments are traded in a particular sector and/or the volume reporting policy for the feeds you are using. Instruments from crypto and forex markets, for example, will often display similar volume on both feeds. Stocks will often display variations because block trades or other types of trades may not be included in their intraday volume data. Futures will also typically display variations. It is even possible that volume from different feeds may not be of the same nature, as you can get trade volume (market volume) on one feed and tick volume (transaction counts) on another. You will sometimes be able to find the details of what different feeds contain from the technical information provided by exchanges/brokers on their feeds. This is an example for the NASDAQ feeds . Once you determine which feeds you are using, you can look for the reporting specs for that feed. This is all research you will need to do on your own; "Volume X-ray" will not help you with that part.
You may elect to forego the deep dive in feed information and simply rely on the figure the indicator will calculate for the instruments you trade. One simple — and unproven — way to interpret "Volume X-ray" values is to infer that instruments with larger percentages of intraday/EOD volume ratios are more "democratic" because at intraday timeframes, you are seeing a greater proportion of the actual traded volume for the instrument. This could conceivably lead one to conclude that such volume data is more reliable than on an instrument where intraday volume accounts for only 3% of EOD volume, let's say.
Note that as intraday vs EOD variations exist for historical bars on some instruments, there will typically also be differences between the realtime feeds used on intraday vs 1D or greater timeframes for those same assets. Realtime reporting rules will often be different from historical feed reporting rules, so variations between realtime feeds will often be different from the variations between historical feeds for the same instrument. A deep dive in reporting rules will quickly reveal what a jungle they are for some instruments, yet it is the only way to really understand the volume information our charts display.
█ HOW TO USE IT
The script is very simple and has no inputs. Just add it to 1D charts and it will calculate the proportion of volume reported on the intraday feed over the EOD volume. The plots show the daily values for both volumes: the teal area is the EOD volume, the orange line is the intraday volume. A value representing the average, cumulative intraday/EOD volume percentage for the chart is displayed in the upper-right corner. Its background color changes with the percentage, with brightness levels proportional to the percentage for both the bull color (% >= 50) or the bear color (% < 50). When abnormal conditions are detected, such as missing volume of one kind or the other, a yellow background is used.
Daily and cumulative values are displayed in indicator values and the Data Window.
The indicator loads in a pane, but you can also use it in overlay mode by moving it on the chart with "Move to" in the script's "More" menu, and disabling the plot display from the "Settings/Style" tab.
█ LIMITATIONS
• The script will not run on timeframes >1D because it cannot produce useful values on them.
• The calculation of the cumulative average will vary on different intraday timeframes because of the varying number of days covered by the dataset.
Variations can also occur because of irregularities in reported volume data. That is the reason I recommend using it on 1D charts.
• The script only calculates on historical bars because in real time there is no distinction between intraday and EOD feeds.
• You will see plenty of special cases if you use the indicator on a variety of instruments:
• Some instruments have no intraday volume, while on others it's the opposite.
• Missing information will sometimes appear here and there on datasets.
• Some instruments have higher intraday than EOD volume.
Please do not ask me the reasons for these anomalies; it's your responsibility to find them. I supply a tool that will spot the anomalies for you — nothing more.
█ FOR PINE CODERS
• This script uses a little-known feature of request.security() , which allows us to specify `"1440"` for the `timeframe` argument.
When you do, data from the 1min intrabars of the historical intraday feed is aggregated over one day, as opposed to the usual EOD feed used with `"D"`.
• I use gaps on my request.security() calls. This is useful because at intraday timeframes I can cumulate non- na values only.
• I use fixnan() on some values. For those who don't know about it yet, it eliminates na values from a series, just like not using gaps will do in a request.security() call.
• I like how the new switch structure makes for more readable code than equivalent if structures.
• I wrote my script using the revised recommendations in the Style Guide from the Pine v5 User Manual.
• I use the new runtime.error() to throw an error when the script user tries to use a timeframe >1D.
Why? Because then, my request.security() calls would be returning values from the last 1D intrabar of the dilation of the, let's say, 1W chart bar.
This of course would be of no use whatsoever — and misleading. I encourage all Pine coders fetching HTF data to protect their script users in the same way.
As tool builders, it is our responsibility to shield unsuspecting users of our scripts from contexts where our calcs produce invalid results.
• While we're on the subject of accessing intrabar timeframes, I will add this to the intention of coders falling victim to what appears to be
a new misconception where the mere fact of using intrabar timeframes with request.security() is believed to provide some sort of edge.
This is a fallacy unless you are sending down functions specifically designed to mine values from request.security() 's intrabar context.
These coders do not seem to realize that:
• They are only retrieving information from the last intrabar of the chart bar.
• The already flawed behavior of their scripts on historical bars will not improve on realtime bars. It will actually worsen because in real time,
intrabars are not yet ordered sequentially as they are on historical bars.
• Alerts or strategy orders using intrabar information acquired through request.security() will be using flawed logic and data most of the time.
The situation reminds me of the mania where using Heikin-Ashi charts to backtest was all the rage because it produced magnificent — and flawed — results.
Trading is difficult enough when doing the right things; I hate to see traders infected by lethal beliefs.
Strive to sharpen your "herd immunity", as Lionel Shriver calls it. She also writes: "Be leery of orthodoxy. Hold back from shared cultural enthusiasms."
Be your own trader.
█ THANKS
This indicator would not exist without the invaluable insights from Tim, a member of the Pine team. Thanks Tim!
EXAMPLES:enhanced_taThis script is created to demonstrate usage of enhanced ta library which is present here :
Following custom indicators are populated in this script:
ma (Select moving average)
atr/atrpercent (With custom moving average)
bands - Bollinger Band, Keltner Channel, Donchian Channel (All with enhanced versions and additional options)
bandwidth - Bandwidth for the bands available. Uses same input as that of bands
bandpercent - Percentage in relation to band upper and lower levels. Uses same input as that of bands.
oscillator (oscillatorRange) - Generating custom overbought oversold regions.
Display Options
Display individual indicator by selecting them through dropdown. If you select all, we also look at overlay and non-overlay parameters to show/hide only those indicators which are applicable on candle overlay or as separate window.
Bitcoin - CME Futures Friday Close
This indicator displays the weekly Friday closing price according to the CME trading hours (Friday 4pm CT).
A horizontal line is displayed until the CME opens again on Sunday 5pm CT.
This indicator is based on the thesis, that during the weekend the Bitcoin price tends to mean reverse to the CME closing price of the prior Friday. The level can also act as support/resistance. This indicator gives a visualization of this key level for the relevant time window.
Furthermore the indicator helps to easily identify, if there is an up or down gap in the CME Bitcoin contract.
Percentile - Price vs FundamentalsThis is done in the same lines of below scripts
Drawdown-Price-vs-Fundamentals
Drawdown-Range
Instead of using drawdown, here we are only plotting percentile of drawdown. Also added few more fundamental stats to the indicator. Also using part of the code from Random-Color-Generator/ to automatically generate colors. This in turn uses code from @RicardoSantos for convering color based on HSL to RGB
This is how the study can be used:
Study plots percentile of price and each of the listed fundamentals based on history. History can be chose All time or particular window. If any fundamental or price is near 100 - which means it is nearer to its peak. And if something is near its bottom, it is nearer to its 0th percentile.
Price of the stock is considered undervalued based on historical levels when it is below most of the fundamentals. Price is considered overvalued based on historical levels when it is above all the fundamentals. Please note, being undervalued does not guarantee immediate mean reversion. Stocks can stay undervalued for prolonged time and can go further down. Similarly overvalued stock can stay overvalued for prolonged time before correcting itself or justifying the position. Hence, further discretion needs to be used while using this study.
Few examples:
AMZN seems to be trading in range and so are the fundamentals:
MSFT at peak along with half of the fundamentals. But, debt levels are going up along with margins reducing.
LPX is trading at 15% discount whereas most of the fundamentals are at the peak.
FLGT price seems to have gone down further whereas fundamentals look pretty healthy.
DEMA/EMA & VOLATILITY (VAMS)The biggest issue with momentum following strategies is over signaling during whipsaw periods. I created this strategy that measure momentum with DEMA (Fast Moving) and EMA (Slow moving). In order to mitigate over signaling during whipsaw periods I implemented the average true range percentage (ATRP) to measure realized volatility. If momentum is picking up while volatility is under a certain threshold it purchases the security. If momentum slows while volatility picks up it sells the security. Additionally, if momentum picks up, but volatility is high, it stays out of the security. This follows the theory that during sustained uptrends volatility will decrease, and during market corrections the volatility picks up. Following the old adage that markets climb up the stairs, and fall out the window. Note that this strategy does repaint due to it entering and closing positions at the close of the bars. I forgot to mention how volatility is measured high vs low. If the ATRP is above the EMA of the ATRP the strategy interprets the volatility is increasing and does not enter the security & Vice Versa for selling (with momentum signal of MAs)
This is just my first strategy, any feedback would be much appreciated.
Ichimoku breakoutIf you use Ichimoku Cloud strategies, this indicator is very useful for you!
This code indicates the candles that break the ichimoku cloud in both directions!
conversion line, base line and lagging span are disable by default, you can enable it from settings window.
green triangles under the candles with green backgrounds show break out the red clouds.
red triangles at the top of the candles with red backgrounds show break out the red clouds.
you can set alerts to be notified when an Ichimoku Cloud is broken.
[blackcat] L2 Ehlers Adaptive Jon Andersen R-Squared IndicatorLevel: 2
Background
@pips_v1 has proposed an interesting idea that is it possible to code an "Adaptive Jon Andersen R-Squared Indicator" where the length is determined by DCPeriod as calculated in Ehlers Sine Wave Indicator? I agree with him and starting to construct this indicator. After a study, I found "(blackcat) L2 Ehlers Autocorrelation Periodogram" script could be reused for this purpose because Ehlers Autocorrelation Periodogram is an ideal candidate to calculate the dominant cycle. On the other hand, there are two inputs for R-Squared indicator:
Length - number of bars to calculate moment correlation coefficient R
AvgLen - number of bars to calculate average R-square
I used Ehlers Autocorrelation Periodogram to produced a dynamic value of "Length" of R-Squared indicator and make it adaptive.
Function
One tool available in forecasting the trendiness of the breakout is the coefficient of determination (R-squared), a statistical measurement. The R-squared indicates linear strength between the security's price (the Y - axis) and time (the X - axis). The R-squared is the percentage of squared error that the linear regression can eliminate if it were used as the predictor instead of the mean value. If the R-squared were 0.99, then the linear regression would eliminate 99% of the error for prediction versus predicting closing prices using a simple moving average.
When the R-squared is at an extreme low, indicating that the mean is a better predictor than regression, it can only increase, indicating that the regression is becoming a better predictor than the mean. The opposite is true for extreme high values of the R-squared.
To make this indicator adaptive, the dominant cycle is extracted from the spectral estimate in the next block of code using a center-of-gravity ( CG ) algorithm. The CG algorithm measures the average center of two-dimensional objects. The algorithm computes the average period at which the powers are centered. That is the dominant cycle. The dominant cycle is a value that varies with time. The spectrum values vary between 0 and 1 after being normalized. These values are converted to colors. When the spectrum is greater than 0.5, the colors combine red and yellow, with yellow being the result when spectrum = 1 and red being the result when the spectrum = 0.5. When the spectrum is less than 0.5, the red saturation is decreased, with the result the color is black when spectrum = 0.
Construction of the autocorrelation periodogram starts with the autocorrelation function using the minimum three bars of averaging. The cyclic information is extracted using a discrete Fourier transform (DFT) of the autocorrelation results. This approach has at least four distinct advantages over other spectral estimation techniques. These are:
1. Rapid response. The spectral estimates start to form within a half-cycle period of their initiation.
2. Relative cyclic power as a function of time is estimated. The autocorrelation at all cycle periods can be low if there are no cycles present, for example, during a trend. Previous works treated the maximum cycle amplitude at each time bar equally.
3. The autocorrelation is constrained to be between minus one and plus one regardless of the period of the measured cycle period. This obviates the need to compensate for Spectral Dilation of the cycle amplitude as a function of the cycle period.
4. The resolution of the cyclic measurement is inherently high and is independent of any windowing function of the price data.
Key Signal
DC --> Ehlers dominant cycle.
AvgSqrR --> R-squared output of the indicator.
Remarks
This is a Level 2 free and open source indicator.
Feedbacks are appreciated.
Relative VolumeVolume can be a very useful tool if used correctly. Relative volume is designed to filter out the noise and highlight anomalies assisting traders in tracking institutional movements. This tool can be used to identify stop loss hunters and organized dumps. It uses a variety of moving averages to hide usual activity and features an LSMA line to show trend. Trend columns are shown to highlight activity and can be seen at bottom of the volume columns, this is done using ZLSMA and LSMA.
The above chart shows an example of 2 indicators being used on the 15 min chart. The bottom indicator is set to the 1 min chart. Traders can see a large dump on the 1 min chart as institutions wipe out any tight stop losses. Next they buy back in scooping up all those long positions.
This is an example layout using a split screen setup and multiple timeframes ranging from 1 min to 30 mins. This gives a clear indication of trends and make it easy to pickup on institutional behaviour. Tip: Double clicking indicator background will maximize RVOL to the split screen window.
Example - Custom Defined Dual-State SessionThis script example aims to cover the following:
defining custom timeframe / session windows
gather a price range from the custom period ( high/low values )
create a secondary "holding" period through which to display the data collected from the initial session
simple method to shift times to re-align to preferred timezone
Articles and further reading:
www.investopedia.com - trading session
Reason for Study:
Educational purposes only.
Before considering writing this example I had seen multiple similar questions
asking how to go about creating custom timeframes or sessions, so it seemed
this might be a good topic to attempt to create a relatively generic example.
MA DerivativesMA Derivatives basicly using Ichimoku Cloud and some additional moving averages for traders.
A. ICHIMOKU
Tenkan-sen (Conversion Line): (9-period high + 9-period low)/2
On a daily chart , this line is the midpoint of the 9-day high-low range, which is almost two weeks.
Kijun-sen (Base Line): (26-period high + 26-period low)/2
On a daily chart , this line is the midpoint of the 26-day high-low range, which is almost one month.
Senkou Span A (Leading Span A): (Conversion Line + Base Line)/2
This is the midpoint between the Conversion Line and the Base Line. The Leading Span A forms one of the two Cloud boundaries. It is referred to as “Leading” because it is plotted 26 periods in the future and forms the faster Cloud boundary.
Senkou Span B (Leading Span B): (52-period high + 52-period low)/2
On the daily chart , this line is the midpoint of the 52-day high-low range, which is a little less than 3 months. The default calculation setting is 52 periods, but it can be adjusted. This value is plotted 26 periods in the future and forms the slower Cloud boundary.
Chikou Span: Represents the closing price and is plotted 26 days back.
Kumo Cloud: Kumo cloud between Senkuo Span A and Senkou Span B lines. It can be green or red. Color can be change with the trend.
You can use Ichimoku for buy&sell strategy
For Buying Strategy
- Tenkansen (Conversion Line) should crossover Kijunsen (Base line) above the highest line of cloud
- Price should be above the highest line of cloud
- Chikouspan should be above the cloud
For Selling Strategy
- Kijunsen (Base Line) should crossover Tenkansen (Conversion Line) below the lowest line of cloud
- Price should be below the lowest line of cloud
- Chikouspan should be below the cloud
B. SIMPLE MOVING AVERAGES
The indicator has some of Simple Moving Averages
It includes:
-Simple Moving Average 50
-Simple Moving Average 100
-Simple Moving Average 200
C. EXPONENTIAL MOVING AVERAGES
The indicator has some of Simple Moving Averages
It includes:
-Exponential Moving Average 9
-Exponential Moving Average 21
-Exponential Moving Average 50
D. BOLLINGER BAND
Bollinger Bands are a type of price envelope developed by John BollingerOpens in a new window. (Price envelopes define upper and lower price range levels.) Bollinger Bands are envelopes plotted at a standard deviation level above and below a simple moving average of the price. Because the distance of the bands is based on standard deviation, they adjust to volatility swings in the underlying price.
Bollinger Bands use 2 parameters, Period and Standard Deviations, StdDev. The default values are 20 for period, and 2 for standard deviations, although you may customize the combinations.
Bollinger bands help determine whether prices are high or low on a relative basis. They are used in pairs, both upper and lower bands and in conjunction with a moving average. Further, the pair of bands is not intended to be used on its own. Use the pair to confirm signals given with other indicators.
How this indicator works
When the bands tighten during a period of low volatility, it raises the likelihood of a sharp price move in either direction. This may begin a trending move. Watch out for a false move in opposite direction which reverses before the proper trend begins.
When the bands separate by an unusual large amount, volatility increases and any existing trend may be ending.
Prices have a tendency to bounce within the bands' envelope, touching one band then moving to the other band. You can use these swings to help identify potential profit targets. For example, if a price bounces off the lower band and then crosses above the moving average, the upper band then becomes the profit target.
Price can exceed or hug a band envelope for prolonged periods during strong trends. On divergence with a momentum oscillator, you may want to do additional research to determine if taking additional profits is appropriate for you.
A strong trend continuation can be expected when the price moves out of the bands. However, if prices move immediately back inside the band, then the suggested strength is negated.
Calculation
First, calculate a simple moving average. Next, calculate the standard deviation over the same number of periods as the simple moving average. For the upper band, add the standard deviation to the moving average. For the lower band, subtract the standard deviation from the moving average.
Typical values used:
Short term: 10 day moving average, bands at 1.5 standard deviations. (1.5 times the standard dev. +/- the SMA)
Medium term: 20 day moving average, bands at 2 standard deviations.
Long term: 50 day moving average, bands at 2.5 standard deviations.
E. ADJUSTABLE MOVING AVERAGES
And this script has also 2 adjustable moving average
- 1 Adjustable Simple Moving Average
- 1 Adjustable Exponential Moving Average
You can just change the length for using this tool.
Wave Trend w/ VWMA overlayThis is a trend-following strategy and indicator which combines the Wave Trend Strategy (Lazy Bear) by thomas.gigure with the cRSI + Waves Strategy with VWMA overlay by Dr_Roboto .
You may update the parameters of the Wave Trend oscillator or the VWMA indicator to match your own preferences. You may also adjust the Base Quantity used for determining trade size (as described below) to suit your account size and risk tolerance.
The strategy identifies potential signals based on the on the Wave Trend oscillator, originally ported to TradingView by LazyBear. When a signal is produced by the Wave Trend oscillator, trade size is determined by the VWMA.
When the VWMA is trending against the direction of the Wave Trend signal, Base Quantity x 1 is used
When the VWMA is trending neutral, Base Quantity x 2 is used
When the VWMA is trending with the direction of the Wave Trend signal, Base Quantity x 4 is used
The strategy includes the ability to limit trade signals to certain defined periods of time ("Sessions") during the trading day and, optionally, to close any open position at the end of either or both "Sessions." This may be enabled/disabled via the Limit Signals to Trading Sessions? option on the "Inputs" tab of the strategy's "Settings" window.
If you are trading on a daily chart (or longer) you must disable the Limit Signals to Trading Sessions? in order for the strategy to produce signals.
RSI & MACDThis indicator presents standart RSI and MACD indicators in a single indicator. The appearances of these indicators have been modified a little bit and squeezed into one window. To overcome the scale problem the MACD values has expanded with 1000 and divided by the current price to use both indicators in the same scale. Original values could be determined from there. Original Tradingview codes have been used to get full control of graphs.
Bu indikatör RSI ve MACD gösterfgelerini tek bir indikatörde sunuyor. İndikatörlerin görünüşleri bir miktar modifiye edilip iki indikatörğn tek bir pencereden takip edilmesine olanak sağlanmıştır. İki indikatördeki farklı ölçek kullanımından ortaya çıkan ölçek sorunu MACD değerlerinin 1000 ile genişletilip, ürünün güncel fiyatına bölünmesiyle giderilmiştir. Her iki indikatiör için de orjinal Tradingview kodları kullanılmıştır.
Indices Sector SigmaSpikes█ OVERVIEW
“The benchmark Dow Jones Industrial Average is off nearly 300 points as of midday today...”
“So what? Is that a lot or a little? Should we care?”
-Adam H Grimes-
This screener aims to provide Bird-Eye view across sector indices, to find which sector is having significant or 'out-of-norm' move in either direction.
The significance of the move is measured based on Sigma Spikes, a method proposed by Adam H. Grimes, where Standard Deviation of returns used as a baseline.
*You can google his blog or read his book, got some gold in there, especially on how he use indicators for trading
█ Understanding Sigma Spikes
As described by Grimes, moves in markets are only meaningful when we consider what “normal” is for that market.
Without that baseline, the daily change number, and even the percent change on the day doesn’t really mean much.
To overcome that problem, Sigma Spikes, as a measure of volatility, attempt to put todays change in price (aka return) in context of the standard deviation of 20 days daily's return.
Refer chart below:
1. The blue bars refer to each days return
2. The orange line is 1 time standard deviation of past 20days daily's return (today not included)
3. The red line is 2 time standard deviation of past 20days daily's return (today not included)
Using the ratio of today's return over the Std Deviation, determining your threshold (1,2,3,etc) will be the key that tells if today's move is significant or not.
*Threshold referring to times standard deviation, and different market may require different threshold.
*20 Days period are based on the Lookback Period, adjustable from user input window.
█ Features
- Scan up to 13 symbols at a time (Bursa (MYX) indices are defaulted, but you may change to any symbols/index from the user input setting)
█ Limitation
- Due to multiple use of security() function required to call other symbols, expect the screener to be slow at certain times
- Custom Timeframe currently accept only Daily and Weekly. I'll try to include lower timeframe in the next update
█ Disclaimer
Past performance is not an indicator of future results.
My opinions and research are my own and do not constitute financial advice in any way whatsoever.
Nothing published by me constitutes an investment recommendation, nor should any data or Content published by me be relied upon for any investment/trading activities.
I strongly recommends that you perform your own independent research and/or speak with a qualified investment professional before making any financial decisions.
Any ideas to further improve this indicator are welcome :)
